As one kind of skin cancer, melanoma is very dangerous. Dermoscopy-based early detection and reorganization strategy is critical for melanoma therapy. Skin imaging is essential in order to diagnose the tumor type; this had to be accomplished by taking a biopsy from patients. This procedure is very uncomfortable for patients and requires time and highly qualified dermatologists to precisely differentiate between different types of skin cancer. In order to solve these problems, many efforts focused on developing automatic image analysis systems that reduce the suffering of patients, time, and tumor classification error. Here we report a novel strategy based on deep learning technique, which achieved very high skin melanoma diagnosis accuracy. Different pretrained architectures (VGG16, Inception_v3, and ResNet-50) were proposed and applied on 10,135 dermoscopy skin images. A comparison between these models was accomplished with hair removal and nonremoval algorithms. Experimental results showed that the removal of hair had a significant effect on the skin imaging as it helped to improve the training accuracy results. The VGG16 model achieved the highest accuracy (96.9%) with hair removal algorithm, followed by Inception_v3 and ResNet-50 model with an accuracy of 94.2% and 91.8% respectively.